The problem of suicidal ideation detection is often formulated in binary and multiclass classification. Binary suicidal classification determines whether a given post contains suicidal thoughts or a user is at risk of suicide. Figure REF  shows that in most studies data is labeled as a binary classification of 0 (non-suicidal), or 1(suicidal) [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}. Shah et al. labeled data according to the source assigning the label "1" to the data that are from Suicide-Watch and "0" to the data from other sub-Reddit forums [1]}. Jain et al. labeled the second dataset (Reddit and Twitter) using two classes, risky and non-risky and annotated the first dataset (questionnaire) using five levels of depression severity [11]}.
Multiclass classification tasks are formulated based on the assumption that each sample is assigned to several pre-defined classes. For example, O'Dea et al. classified posts into three levels: "Strongly concerning," "Possibly concerning," and "Safe to ignore." Annotators were instructed to choose only one of the three levels and, if in doubt, to choose "Safe to ignore" [12]}. Four levels of classification were used by Vioules et al. ranging from normal to suicidal [13]}. Burnap et al. classified Twitter text into seven classes, including suicidal intent, or other suicide-related topics such as suicide campaigning, support or prevention of suicidality, reporting of suicide, flippant reference to suicide, or none of the above [14]}, [15]}, [16]}.
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